Scalably Using Node Attributes and Graph Structure for Node Classification [PDF]
Entropy, 2022 The task of node classification concerns a network where nodes are associated with labels, but labels are known only for some of the nodes. The task consists of inferring the unknown labels given the known node labels, the structure of the network, and ...
Arpit Merchant +2 more
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Active Learning for Node Classification: An Evaluation [PDF]
Entropy, 2020 Current breakthroughs in the field of machine learning are fueled by the deployment of deep neural network models. Deep neural networks models are notorious for their dependence on large amounts of labeled data for training them. Active learning is being
Kaushalya Madhawa, Tsuyoshi Murata
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Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification [PDF]
Frontiers in Neurorobotics, 2021 The graph neural network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered.
Shuhao Shi +5 more
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SS-AdaMoE: Spatio-Spectral Adaptive Mixture of Experts with Global Structural Priors for Graph Node Classification [PDF]
EntropyGraph Neural Networks (GNNs) have emerged as the standard for learning representations from graph-structured data. While traditional architectures relying on message-passing mechanisms excel in homophilic settings, they essentially function as fixed low ...
Xilin Kang +4 more
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DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification [PDF]
EntropyThis paper introduces DeeWaNA, an unsupervised network representation learning framework that unifies random walk strategies and neighborhood aggregation mechanisms to improve node classification performance.
Xin Xu, Xinya Lu, Jianan Wang
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Hierarchical Structure-Feature Aware Graph Neural Network for Node Classification
IEEE Access, 2022 In recent years, graph neural network is used to process graph data and has been successfully applied to graph node classification task. Due to the complexity of graph structure and the difficulty of obtaining node labels, node classification in datasets
Wenbin Yao +3 more
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Hierarchical Model Selection for Graph Neural Networks
IEEE Access, 2023 Node classification on graph data is a major problem in machine learning, and various graph neural networks (GNNs) have been proposed. Variants of GNNs such as H2GCN and CPF outperform graph convolutional networks (GCNs) by improving on the weaknesses of
Yuga Oishi, Ken Kaneiwa
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Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities
Mathematics, 2022 Nowadays, graph representation learning methods, in particular graph neural network methods, have attracted great attention and performed well in many downstream tasks. However, most graph neural network methods have a single perspective since they start
Xing Li +3 more
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Network Representation Learning With Community Awareness and Its Applications in Brain Networks
Frontiers in Physiology, 2022 Previously network representation learning methods mainly focus on exploring the microscopic structure, i.e., the pairwise relationship or similarity between nodes.
Min Shi, Bo Qu, Xiang Li, Cong Li
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Unsupervised Graph Representation Learning With Variable Heat Kernel
IEEE Access, 2020 Graph representation learning aims to learn a low-dimension latent representation of nodes, and the learned representation is used for downstream graph analysis tasks. However, most of the existing graph embedding models focus on how to aggregate all the
Yongjun Jing +4 more
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